Electrothermal modeling of GaN high electron mobility transistors using a Monte Carlo-trained hybrid AI-thermal approach with microscopic physical insight
[EN]Self-heating significantly impacts the performance and reliability of GaN high electron mobility transistors, but capturing these effects with electrothermal Monte Carlo (MC) simulations is computationally intensive. This paper presents the application of a hybrid AI-thermal model, previously te...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/168892 |
| Acceso en línea: | http://hdl.handle.net/10366/168892 |
| Access Level: | acceso abierto |
| Palabra clave: | Electronic transport Two-dimensional electron gas Semiconductors Field effect transistors Heterostructures Deep learning Artificial neural networks Monte Carlo methods |
| Sumario: | [EN]Self-heating significantly impacts the performance and reliability of GaN high electron mobility transistors, but capturing these effects with electrothermal Monte Carlo (MC) simulations is computationally intensive. This paper presents the application of a hybrid AI-thermal model, previously tested on another device, to the electrothermal analysis of GaN HEMTs. This first component consists of an artificial neural network (ANN) trained on isothermal MC data to predict drain current and lattice temperature. To extend the framework, a set of ANN-based microscopic models is introduced, composed of three dedicated networks that reconstruct spatially resolved quantities—electric field, carrier velocity, and sheet electron density. The system is coupled with compact thermal resistance models and iterated until convergence. The proposed approach achieves excellent agreement with electrothermal MC simulations while reducing computation time by approximately an order of magnitude. In addition to global performance metrics, it provides detailed internal profiles under electrothermally consistent conditions, making it a practical tool for fast device evaluation, in-depth analysis, and integration into compact modeling flows. |
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